SoroushVahidi/scidocs huggingface dataset:
Development of a Compact Switched-Reluctance Motor Drive for EV Propulsion With Voltage-Boosting and PFC Charging Capabilities
(2009)

This paper presents a compact battery-powered switched-reluctance motor (SRM) drive for an electric vehicle with voltage-boosting and on-board power-factor-corrected-charging capabilities. Although the boost-type front-end DC/DC converter is externally equipped, the on-board charger is formed by the embedded components of SRM windings and converter. In the driving mode, the DC/DC boost converter is controlled to establish boostable well-regulated DC-link voltage for the SRM drive from the battery. During demagnetization of each stroke, the winding stored energy is automatically recovered back to the battery. Moreover, the regenerative braking is achieved by properly setting the SRM in the regenerating mode. The controls of two power stages are properly designed and digitally realized using a common digital signal processor. Good winding current and speed dynamic responses of the SRM drive are obtained. In addition, the static operation characteristics are also improved. In the charging mode, the power devices that were embedded in the SRM converter and the three motor-phase windings are used to form a buck-boost switch-mode rectifier to charge the battery from utility with good power quality. All constituted circuit components of the charger are placed on board, and only the insertion of power cords is needed.
On the Mathematics of Flat Origamis∗
(1994)

Origami is the art of folding pieces of paper into works of sculpture without the aid of scissors or glue. Modern advancements in the complexity of origami (e.g., the work of Montroll and Maekawa) reveal a rich geometric structure governing the possibilities of paperfolding. In this paper we initiate a mathematical study of this “origami geometry” and explore the possibilities of a graph theoretic model. In particular, we study the properties of origami models which fold flat (i.e., can be pressed in a book without crumpling). Necessary and sufficient conditions are given for an origami model to locally fold flat, and the problems encountered in trying to extend these results globally are discussed.
Cocaine cues and dopamine in dorsal striatum: mechanism of craving in cocaine addiction.
(2006)

The ability of drugs of abuse to increase dopamine in nucleus accumbens underlies their reinforcing effects. However, preclinical studies have shown that with repeated drug exposure neutral stimuli paired with the drug (conditioned stimuli) start to increase dopamine by themselves, which is an effect that could underlie drug-seeking behavior. Here we test whether dopamine increases occur to conditioned stimuli in human subjects addicted to cocaine and whether this is associated with drug craving. We tested eighteen cocaine-addicted subjects using positron emission tomography and [11C]raclopride (dopamine D2 receptor radioligand sensitive to competition with endogenous dopamine). We measured changes in dopamine by comparing the specific binding of [11C]raclopride when subjects watched a neutral video (nature scenes) versus when they watched a cocaine-cue video (scenes of subjects smoking cocaine). The specific binding of [11C]raclopride in dorsal (caudate and putamen) but not in ventral striatum (in which nucleus accumbens is located) was significantly reduced in the cocaine-cue condition and the magnitude of this reduction correlated with self-reports of craving. Moreover, subjects with the highest scores on measures of withdrawal symptoms and of addiction severity that have been shown to predict treatment outcomes, had the largest dopamine changes in dorsal striatum. This provides evidence that dopamine in the dorsal striatum (region implicated in habit learning and in action initiation) is involved with craving and is a fundamental component of addiction. Because craving is a key contributor to relapse, strategies aimed at inhibiting dopamine increases from conditioned responses are likely to be therapeutically beneficial in cocaine addiction.
Detection of data injection attack in industrial control system using long short term memory recurrent neural network
(2018)

In 2010, the outbreak of Stuxnet sounded a warning in the field of industrial control.security. As the major attack form of Stuxnet, data injection attack is characterized by high concealment and great destructiveness. This paper proposes a new method to detect data injection attack in Industrial Control Systems (ICS), in which Long Short Term Memory Recurrent Neural Network (LSTM-RNN) is a temporal sequences predictor. We then use the Euclidean detector to identify attacks in a model of a chemical plant. With simulation and evaluation in Tennessee Eastman (TE) process, we show that this method is able to detect various types of data injection attacks.
Theorieentwicklung in der Akzeptanzforschung: Entwicklung eines Modells auf Basis einer qualitativen Studie
(2013)

Die Untersuchung der Akzeptanz von Technologien im Allgemeinen und Software im Besonderen ist ein fruchtbares Feld in der angloamerikanischen Forschung der Disziplinen (Management) Information Systems und der deutschen Wirtschaftsinformatik. Trotz zahlreicher Untersuchungen, die ihren Ursprung in dem Technologie-Akzeptanzmodell und verwandten Theorien haben, mehren sich Beiträge, welche die Defizite bisherigerer Studien und Forschungsansätze herausstellen. Eine wesentliche Ursache ist Fokussierung auf quantitative Forschungsmethoden, wie wir anhand von Metastudien und einer eigenen Literaturrecherche aufzeigen. Während quantitative Verfahren sich in der Regel gut für eine Überprüfung gegebener Theorien eignen, ist ihr Beitrag für die Bildung neuer Theorien begrenzt. Im vorliegenden Beitrag wird aufgezeigt, wie ein qualitatives Verfahren zur besseren Theoriebildung genutzt werden kann. Am Beispiel der Untersuchung der Akzeptanz von Projektmanagement-Software (PMS) kann aufgezeigt werden, dass dieses Vorgehen zu neuen Konstrukten führt, während einige Konstrukte bestehender Akzeptanz-Theorien nicht bestätigt werden konnten.
A Kalman Filter-Based Algorithm for IMU-Camera Calibration: Observability Analysis and Performance Evaluation
(2008)

Vision-aided inertial navigation systems (V-INSs) can provide precise state estimates for the 3-D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved by combining inertial measurements from an inertial measurement unit (IMU) with visual observations from a camera under the assumption that the rigid transformation between the two sensors is known. Errors in the IMU-camera extrinsic calibration process cause biases that reduce the estimation accuracy and can even lead to divergence of any estimator processing the measurements from both sensors. In this paper, we present an extended Kalman filter for precisely determining the unknown transformation between a camera and an IMU. Contrary to previous approaches, we explicitly account for the time correlation of the IMU measurements and provide a figure of merit (covariance) for the estimated transformation. The proposed method does not require any special hardware (such as spin table or 3-D laser scanner) except a calibration target. Furthermore, we employ the observability rank criterion based on Lie derivatives and prove that the nonlinear system describing the IMU-camera calibration process is observable. Simulation and experimental results are presented that validate the proposed method and quantify its accuracy.
Estimation and Removal of Clock Skew from Network Delay Measurements
(1999)

Packet delay and loss traces are frequently used by network engineers, as well as network applications, to analyze netw ork performance. The clocks on the end-systems used to measure the dela ys, however, are not always synchronized, and this lack of synchron ization reduces the accuracy of these measurements. Therefore, estimating and removing relative skews and offsets from delay measurements between sender and receiver clocks are critical to the accurate assessment and analysis of network performance. In this paper we introduce a linear programming-based algorithm to estimate the clock skew in network delay measur ements and compare it with three other algorithms. We show that our algorithm has time complexity of O(N), leaves the delay after the skew removal positive, and is robust in the sense that the error margin of the skew est imate is independent of the magnitude of the skew. We use traces of real Int ernet delay measurements to assess the algorithm, and compare its perfo rmance to that of three other algorithms. Furthermore, we show through simulation that our algorithm is unbiased, and that the sample variance of the skew estimate is better (smaller) than existing algorithms. Keywords— clock skew, clock ratio, end-to-end delay, delay measurement.
A synchronized visual-inertial sensor system with FPGA pre-processing for accurate real-time SLAM
(2014)

Robust, accurate pose estimation and mapping at real-time in six dimensions is a primary need of mobile robots, in particular flying Micro Aerial Vehicles (MAVs), which still perform their impressive maneuvers mostly in controlled environments. This work presents a visual-inertial sensor unit aimed at effortless deployment on robots in order to equip them with robust real-time Simultaneous Localization and Mapping (SLAM) capabilities, and to facilitate research on this important topic at a low entry barrier. Up to four cameras are interfaced through a modern ARM-FPGA system, along with an Inertial Measurement Unit (IMU) providing high-quality rate gyro and accelerometer measurements, calibrated and hardware-synchronized with the images. This facilitates a tight fusion of visual and inertial cues that leads to a level of robustness and accuracy which is difficult to achieve with purely visual SLAM systems. In addition to raw data, the sensor head provides FPGA-pre-processed data such as visual keypoints, reducing the computational complexity of SLAM algorithms significantly and enabling employment on resource-constrained platforms. Sensor selection, hardware and firmware design, as well as intrinsic and extrinsic calibration are addressed in this work. Results from a tightly coupled reference visual-inertial motion estimation framework demonstrate the capabilities of the presented system.
TICSync: Knowing when things happened
(2011)

Modern robotic systems are composed of many distributed processes sharing a common communications infrastructure. High bandwidth sensor data is often collected on one computer and served to many consumers. It is vital that every device on the network agrees on how time is measured. If not, sensor data may be at best inconsistent and at worst useless. Typical clocks in consumer grade PCs are highly inaccurate and temperature sensitive. We argue that traditional approaches to clock synchronization, such as the use of NTP are inappropriate in the robotics context. We present an extremely efficient algorithm for learning the mapping between distributed clocks, which typically achieves better than millisecond accuracy within just a few seconds. We also give a probabilistic analysis providing an upper-bound error estimate.
A 3.3-V single-poly CMOS audio ADC delta-sigma modulator with 98-dB peak SINAD and 105-dB peak SFDR
(2000)

This paper presents a second-order /spl Delta//spl Sigma/ modulator for audio-band analog-to-digital conversion implemented in a 3.3-V, 0.5-/spl mu/m, single-poly CMOS process using metal-metal capacitors that achieves 98-dB peak signal-to-noise-and-distortion ratio and 105-dB peak spurious-free dynamic range. The design uses a low-complexity, first-order mismatch shaping 33-level digital-to-analog converter and a 33-level flash analog-to-digital converter with digital common-mode rejection and dynamic element matching of comparator offsets. These signal-processing innovations, combined with established circuit techniques, enable state-of-the art performance in CMOS technology optimized for digital circuits.
The influence of chronotype and intelligence on academic achievement in primary school is mediated by conscientiousness, midpoint of sleep and motivation.
(2015)

Individuals differ in their timing of sleep (bed times, rise times) and in their preference for morning or evening hours. Previous work focused on the relationship between academic achievement and these variables in secondary school students. The main aim of the study is to investigate the relationship between chronotype and academic achievement in 10-year-old children (n = 1125) attending 4th grade of primary school. They filled a cognitive test (Culture Fair Intelligence Test, CFT 20-R) and questions about rise times and bed times, academic achievement, conscientiousness and motivation. We used the "scales for the assessment of learning and performance motivation" (SELLMO; Skalen zur Erfassung der Lern- und Leistungsmotivation for motivation), the short version of the Five-Factor Personality Inventory Children (FFPI-C) to measure conscientiousness, and the Composite Scale of Morningness (CSM) to assess morningness-eveningness. Mean CSM score was 37.84 ± 6.66, midpoint of sleep was 1:36 ± 00:25 and average sleep duration (time in bed) was 10:15 ± 0:48. Morningness orientation was positively related to intelligence, conscientiousness and learning objectives. Eveningness orientation was related to avoidance performance objectives and work avoidance. Early midpoint of sleep, conscientiousness and intelligence were associated with better grades. The multivariate model showed that intelligence was the strongest predictor of good grades. Conscientiousness, motivation, younger age and an earlier midpoint of sleep were positively related to good grades. This is the first study in primary school pupils, and it shows that the relationship between evening orientation and academic achievement is already prevalent at this age even when controlling for important predictors of achievement.
Learning Policies for First Person Shooter Games Using Inverse Reinforcement Learning
(2011)

The creation of effective autonomous agents (bots) for combat scenarios has long been a goal of the gaming industry. However, a secondary consideration is whether the autonomous bots behave like human players; this is especially important for simulation/training applications which aim to instruct participants in real-world tasks. Bots often compensate for a lack of combat acumen with advantages such as accurate targeting, predefined navigational networks, and perfect world knowledge, which makes them challenging but often predictable opponents. In this paper, we examine the problem of teaching a bot to play like a human in first-person shooter game combat scenarios. Our bot learns attack, exploration and targeting policies from data collected from expert human player demonstrations in Unreal Tournament. We hypothesize that one key difference between human players and autonomous bots lies in the relative valuation of game states. To capture the internal model used by expert human players to evaluate the benefits of different actions, we use inverse reinforcement learning to learn rewards for different game states. We report the results of a human subjects’ study evaluating the performance of bot policies learned from human demonstration against a set of standard bot policies. Our study reveals that human players found our bots to be significantly more human-like than the standard bots during play. Our technique represents a promising stepping-stone toward addressing challenges such as the Bot Turing Test (the CIG Bot 2K Competition).
Service Level Agreement in Cloud Computing
(2009)

Cloud computing that provides cheap and pay-as-you-go computing resources is rapidly gaining momentum as an alternative to traditional IT Infrastructure. As more and more consumers delegate their tasks to cloud providers, Service Level Agreements(SLA) between consumers and providers emerge as a key aspect. Due to the dynamic nature of the cloud, continuous monitoring on Quality of Service (QoS) attributes is necessary to enforce SLAs. Also numerous other factors such as trust (on the cloud provider) come into consideration, particularly for enterprise customers that may outsource its critical data. This complex nature of the cloud landscape warrants a sophisticated means of managing SLAs. This paper proposes a mechanism for managing SLAs in a cloud computing environment using the Web Service Level Agreement(WSLA) framework, developed for SLA monitoring and SLA enforcement in a Service Oriented Architecture (SOA). We use the third party support feature of WSLA to delegate monitoring and enforcement tasks to other entities in order to solve the trust issues. We also present a real world use case to validate our proposal.
Ensemble Named Entity Recognition (NER): Evaluating NER Tools in the Identification of Place Names in Historical Corpora
(2018)

The field of Spatial Humanities has advanced substantially in the past years. The identification and extraction of toponyms and spatial information mentioned in historical text collections has allowed its use in innovative ways, making possible the application of spatial analysis and the mapping of these places with geographic information systems. For instance, automated place name identification is possible with Named Entity Recognition (NER) systems. Statistical NER methods based on supervised learning, in particular, are highly successful with modern datasets. However, there are still major challenges to address when dealing with historical corpora. These challenges include language changes over time, spelling variations, transliterations, OCR errors, and sources written in multiple languages among others. In this article, considering a task of place name recognition over two collections of historical correspondence, we report an evaluation of five NER systems and an approach that combines these through a voting system. We found that although individual performance of each NER system was corpus dependent, the ensemble combination was able to achieve consistent measures of precision and recall, outperforming the individual NER systems. In addition, the results showed that these NER systems are not strongly dependent on preprocessing and translation to Modern English.
Flickr Circles: Aesthetic Tendency Discovery by Multi-View Regularized Topic Modeling
(2016)

Aesthetic tendency discovery is a useful and interesting application in social media. In this paper we propose to categorize large-scale Flickr users into multiple circles, each containing users with similar aesthetic interests (e.g., landscapes). We notice that: (1) an aesthetic model should be flexible as different visual features may be used to describe different image sets; (2) the numbers of photos from different Flickr users vary significantly and some users may have very few photos; and (3) visual features from each Flickr photo should be seamlessly integrated at both low-level and high-level. To meet these challenges, we propose to fuze color, textural, and semantic channel features using a multi-view learning framework, where the feature weights are adjusted automatically. Then, a regularized topic model is developed to quantify each user's aesthetic interest as a distribution in the latent space. Afterward, a graph is constructed to describe the discrepancy of aesthetic interests among users. Apparently, densely connected users are with similar aesthetic interests. Thus, an efficient dense subgraph mining algorithm is adopted to group Flickr users into multiple circles. Experiments have shown that our approach performs competitively on a million-scale image set crawled from Flickr. Besides, our method can enhance the transferal-based photo cropping [40] as reported by the user study.
SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming
(2005)

Support vector machine (SVM) soft margin classifiers are important learning algorithms for classification problems. They can be stated as convex optimization problems and are suitable for a large data setting. Linear programming SVM classifiers are especially efficient for very large size samples. But little is known about their convergence, compared with the well-understood quadratic programming SVM classifier. In this article, we point out the difficulty and provide an error analysis. Our analysis shows that the convergence behavior of the linear programming SVM is almost the same as that of the quadratic programming SVM. This is implemented by setting a stepping-stone between the linear programming SVM and the classical 1-norm soft margin classifier. An upper bound for the misclassification error is presented for general probability distributions. Explicit learning rates are derived for deterministic and weakly separable distributions, and for distributions satisfying some Tsybakov noise condition.
Python robotics: an environment for exploring robotics beyond LEGOs
(2003)

This paper describes Pyro, a robotics programming environment designed to allow inexperienced undergraduates to explore topics in advanced robotics. Pyro, which stands for Python Robotics, runs on a number of advanced robotics platforms. In addition, programs in Pyro can abstract away low-level details such that individual programs can work unchanged across very different robotics hardware. Results of using Pyro in an undergraduate course are discussed.
Heat, vibration, dust, salt spray, weather - taking wireless positioning to the extreme
(2013)

There are lots of local positioning systems available for indoor applications, partly even for outdoor use. Many of them rely on a rather clean environment which does not interfere in their measurements too much. Industrial environments, in- or outdoors, often do not meet these requirements. The positioning applications here are surrounded by dirt, dust and vibrations. The number of local positioning systems being able to perform properly here at all times is limited to such not dependent on optical or sonic measurements. Local secondary radar systems proved to be a reliable source of distance and position measurements, even in harsh environments. This paper examines a few use cases and depicts the performance of local positioning radar systems there.